基于船舶自动识别系统信息和Hough变换的海上船舶航道提取

对远海大面积海域进行航道提取，由于缺少连续的船舶航行数据，传统轨迹聚类算法不再适用。针对该问题，提出了一种利用Hough变换提取船舶航道的方法。基于船舶自动识别系统（AIS）数据，对监视海域划分网格，分析海上船舶密度分布；针对网格大小影响密度分布分辨力问题，采用中值滤波和形态学滤波对船舶密度分布进行修正。基于此利用Hough变换和核密度估计结合的方法提取海上船舶航道，估计航道宽度，用真实历史AIS数据对该方法进行实验验证。实验结果表明：轨迹聚类算法无法提取船舶密度较低区域的航道，轨迹簇内的船舶轨迹数量占该区域轨迹总数的29.81%；而所提方法提取的航道内轨迹数量占比达95.89%，证明了所提方法的有效性。

Vessel traffic pattern extraction based on automatic identification system data and Hough transformation

Traditional trajectory clustering algorithm is no longer applicable due to the lack of continuous ship navigation data for large-scale sea area extraction. To solve this problem, a technique of vessel traffic pattern extraction using Hough transformation was proposed. Based on Automatic Identification System (AIS) data, the target area was divided into grids so that the ship density distribution was analyzed. Considering the problem of density distribution resolution, median filtering and morphological filtering were used to optimize the density distribution. Thus a method combining Hough transformation and Kernel density estimation was proposed to extract vessel traffic pattern and estimate the width of pattern. The experimental verification of the method with real historical AIS data shows that the trajectory clustering method cannot extract vessel traffic pattern in lower ship-density areas, its extracted number of ship trajectories in trajectory clusters accounts for 29.81% of the total number in the area, compared to 95.89% using the proposed method. The experimental result validates the effectiveness of the proposed method.